How to fairly conduct a model performance with 5-fold cross validation after augmentation?
If you used your five $X_{test}$ sets multiple times (to measure the average AUC) to decide on the best set of hyperparameters (i.e. optimizer, learning rate, batch size, dropout, activation) then yes, you successfully conducted hyper-parameter optimization. However, the AUC you received for the best set of hyperparameters found (by manual tuning) is not representative of the real performance of your model. 

This is because the fact of using a test set to tune the parameters of your model degenerates it back to a "training" set, because the data is not being used to measure the performance of the model but to improve it instead (although on a different level of abstraction, i.e. not to directly influence the parameters of the model, such as neural network weights), making the resulting AUC an overly optimistic biased estimator of the real AUC (that it could have resulted in for an unseen test dataset).

That's why, if you care both about hyperparameter optimisation and being able to measure the "real" performance of your model, you need to split your dataset into three "buckets": training set $X_{train}$, validation st $X_{val}$ and test set $X_{test}$. $X_{test}$ should only be used once (after you've trained and tuned the model), and assuming it has enough samples and the samples are representative of the unseen data, you should get a good estimate of your model performance. $X_{val}$, on the other hand, is your validation set which you can reuse as many times as you want to find the optimal set of hyperparameters that result in the highest performance (i.e. AUC).